Biological evolution and creationism from the perspective of graduate students of biological sciences
Bibliographic record
Abstract
This work aimed to analyse students’ conceptions in a graduate course of biology teachers at the University Centre of Formiga, Minas Gerais, Brazil, of topics related to evolutionary theory (Chance and Natural selection) and creationism (God and Intelligent design). We used a part of the European BIOHEAD-CITIZEN questionnaire in a sample of 56 students, studying in their 2nd, 4th and 6th terms. The four-category Barbour model (conflict, independence, dialogue and integration) was used to analyse the data and characterise the students’ ideas of the relationship between science and religion. Using the Pearson chi-square statistical test (χ2), the differences among the groups of students were tested, at the statistical significance level of 5%. The results show that most students are able to establish a relationship of independence between issues of evolutionary theory and creationism. Even religious students can establish boundaries that separate the fields of science and religion. Due to the importance of evolutionary theory for science and for biology in particular, it is necessary for new research to be carried out in the Brazilian context to determine students’ and teachers’ perceptions on the topic and to improve the teaching of evolutionary theory in the biological context and to refrain from inserting personal religious considerations into general science and biology classes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".